Crowdsourcing has emerged as a convenient and an economical method for organizations to outsource certain tasks, which require human involvement. For example, tasks such as digitization of handwritten documents, labeling of images, and anomaly detection in videos, and so on, may be uploaded by a requester on one or more crowdsourcing platforms. Crowdworkers associated with the crowdsourcing platforms may attempt such tasks.
However, crowdsourcing is a complex system, in which performance of the crowdsourcing tasks depends on various input parameters including, but not limited to, worker availability, incentive provided to the workers, compensations strategies deployed by the requesters, and so forth. Further, the performance of the crowdsourcing system may be determined through various output parameters, such as task accuracy, task completion time, task acceptance rate, etc. Various input parameters and the performance of the crowdsourcing systems are generally interdependent, and the performance optimization of the crowdsourcing systems, utilizing this sort of interdependence, has been a non-trivial problem for crowdsourcing service providers.